Evidence map›Paper›PMID 41743490›Full record

ArticleLearning health systems2026

Design and Implementation of a State-Wide Network for Near Real-Time Public Health Surveillance and Research: The Minnesota Electronic Health Record Consortium Experience.

Steven G Johnson, Alanna M Chamberlain, Paul Drawz, R Adams Dudley, Stephen C Waring, Tyler Winkelman, Peter Bodurtha, Kelly Bergmann, Inih Essien, Nayanjot K Rai and 3 more

Abstract read
In one paragraph

Article in Learning health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Steven G JohnsonUniversity of Minnesota Minneapolis Minnesota USA.ORCID https://orcid.org/0000-0002-2983-6384
Alanna M ChamberlainDepartment of Quantitative Health Sciences Mayo Clinic Rochester Minnesota USA.
Paul DrawzUniversity of Minnesota Minneapolis Minnesota USA.
R Adams DudleyUniversity of Minnesota Minneapolis Minnesota USA.
Stephen C WaringEssentia Institute of Rural Health Duluth Minnesota USA.ORCID https://orcid.org/0000-0002-9674-0083
Tyler WinkelmanHennepin Healthcare Research Institute Minneapolis Minnesota USA.ORCID https://orcid.org/0000-0002-9581-5223
Peter BodurthaHennepin Healthcare Research Institute Minneapolis Minnesota USA.
Kelly BergmannChildren's Hospitals and Clinics of Minnesota Minneapolis Minnesota USA.ORCID https://orcid.org/0000-0002-0975-4218
Inih EssienHealthPartners Institute Minneapolis Minnesota USA.
Nayanjot K RaiUniversity of Minnesota Minneapolis Minnesota USA.
Samuel T PatnoeHealthPartners Institute Minneapolis Minnesota USA.
Karen L MargolisHealthPartners Institute Minneapolis Minnesota USA.
MN EHR Consortium

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UM1TR004405 · NCATS · UNIVERSITY OF MINNESOTA · PI Bruce R Blazar, Damien A Fair · 2023 to 2026
$30.8M
NCATS NIH HHS UM1 TR004405
6 · The paper itself

Abstract

Objective: The Minnesota Electronic Health Record Consortium (MNEHRC) was established during the early days of the COVID-19 pandemic to provide data for public health surveillance from the eleven largest health care systems in Minnesota. Methods: This is a descriptive study of the Consortium, which is a federated network that implements best practices for governance and data infrastructure to support public health surveillance and clinical research. We conducted an analysis of the Consortium members, governance structure, infrastructure, and the characteristics of the patient population. Results: The Consortium health systems collect information from 105 hospitals, 773 clinics, 100 emergency departments and 29 040 providers. Information about the health systems and the demographic and clinical characteristics of its 5 471 367 patients is provided, which represents more than 90% of the patients in Minnesota. This manuscript also details the MNEHRC governance structure, working groups, data use agreements and technical infrastructure. The Consortium has produced several studies with state-wide impact. One study, Health Trends Across Communities in Minnesota, is described in detail to illustrate aspects of this collaboration. Conclusion: MNEHRC has been a successful collaboration and vital resource for public health surveillance in the state of Minnesota. Initially, the Consortium focused on surveillance related to COVID-19 infections and vaccinations but has recently expanded into other public health and chronic disease research.

Indexed as

COVIDEHRMinnesotapublic healthresearch consortium

Identifiers

PMID41743490
PMCPMC12929658

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.